Park Jinho, Chung Kwangsue
Department of Electronics and Communications Engineering, Kwangwoon University, Seoul 01897, Korea.
Sensors (Basel). 2021 Dec 20;21(24):8500. doi: 10.3390/s21248500.
Recent years have witnessed a growth in the Internet of Things (IoT) applications and devices; however, these devices are unable to meet the increased computational resource needs of the applications they host. Edge servers can provide sufficient computing resources. However, when the number of connected devices is large, the task processing efficiency decreases due to limited computing resources. Therefore, an edge collaboration scheme that utilizes other computing nodes to increase the efficiency of task processing and improve the quality of experience (QoE) was proposed. However, existing edge server collaboration schemes have low QoE because they do not consider other edge servers' computing resources or communication time. In this paper, we propose a resource prediction-based edge collaboration scheme for improving QoE. We estimate computing resource usage based on the tasks received from the devices. According to the predicted computing resources, the edge server probabilistically collaborates with other edge servers. The proposed scheme is based on the delay model, and uses the greedy algorithm. It allocates computing resources to the task considering the computation and buffering time. Experimental results show that the proposed scheme achieves a high QoE compared with existing schemes because of the high success rate and low completion time.
近年来，物联网（IoT）应用和设备不断增长；然而，这些设备无法满足其所承载应用不断增加的计算资源需求。边缘服务器可以提供足够的计算资源。然而，当连接设备数量众多时，由于计算资源有限，任务处理效率会降低。因此，提出了一种利用其他计算节点来提高任务处理效率并改善体验质量（QoE）的边缘协作方案。然而，现有的边缘服务器协作方案的QoE较低，因为它们没有考虑其他边缘服务器的计算资源或通信时间。在本文中，我们提出了一种基于资源预测的边缘协作方案以提高QoE。我们根据从设备接收到的任务来估计计算资源的使用情况。根据预测的计算资源，边缘服务器以概率方式与其他边缘服务器协作。所提出的方案基于延迟模型，并使用贪心算法。它在考虑计算和缓冲时间的情况下为任务分配计算资源。实验结果表明，与现有方案相比，所提出的方案由于成功率高和完成时间短而实现了较高的QoE。
